MS+PAN: We perform the experiments at \(\text{SNR}_h\)  and \(\text{SNR}_\ell \)  of 30dB and 25 dB respectively. The reconstructions are shown in figure REF  and quality metrics in table REF . Notice that the our method is able to retain fine structures. Although the competing methods were able to superresolve the MS image, the spectral degradation is noticeably large for them. We compare our algorithm with a state-of-the-art variational method [1]}, which is known to have superior performance over deep learning methods such as PNN [2]} and PAN-net [3]}.
Notice that we are competitive with [1]} though the latter method is customized for pansharpening.
